{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试数据集-数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 94 columns</p>\n",
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      ],
      "text/plain": [
       "   id  feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "0   1       0       0       0       0       0       0       0       0       0   \n",
       "1   2       2       2      14      16       0       0       0       0       0   \n",
       "2   3       0       1      12       1       0       0       0       0       0   \n",
       "3   4       0       0       0       1       0       0       0       0       0   \n",
       "4   5       1       0       0       1       0       0       1       2       0   \n",
       "\n",
       "    ...     feat_84  feat_85  feat_86  feat_87  feat_88  feat_89  feat_90  \\\n",
       "0   ...           0        0       11        1       20        0        0   \n",
       "1   ...           0        0        0        0        0        4        0   \n",
       "2   ...           0        0        0        0        2        0        0   \n",
       "3   ...           0        3        1        0        0        0        0   \n",
       "4   ...           0        0        0        0        0        0        0   \n",
       "\n",
       "   feat_91  feat_92  feat_93  \n",
       "0        0        0        0  \n",
       "1        0        2        0  \n",
       "2        0        0        1  \n",
       "3        0        0        0  \n",
       "4        9        0        0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = '../data/'\n",
    "test = pd.read_csv(dpath+'Otto_test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征X\n",
    "test_id = test['id']\n",
    "X = test.iloc[1:]\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "D:\\Program Files\\Anaconda\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "# 标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "X = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存预处理结果\n",
    "test = pd.concat([test_id,pd.DataFrame(columns=feat_names,data=X)],axis=1)\n",
    "test.to_csv('./data/'+'Otto_FE_test.csv',index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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